{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Machine Learning at Scale, Part I"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## KMeans clustering at scale"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Training models with data that fits in memory is very limiting. But minibatch learners can easily work with data directly from disk. \n",
    "\n",
    "We'll use the MNIST data set, which has 8 million images (about 17 GB). The dataset has been partition into groups of 100k images (using the unix split command) and saved in compressed lz4 files. This dataset is very large and doesnt get loaded by default by <code>getdata.sh</code>. You have to load it explicitly by calling <code>getmnist.sh</code> from the scripts directory. The script automatically splits the data into files that are small enough to be loaded into memory. \n",
    "\n",
    "Let's load BIDMat/BIDMach"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1 CUDA device found, CUDA version 7.0\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "(0.97123164,12514033664,12884705280)"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import BIDMat.{CMat,CSMat,DMat,Dict,IDict,Image,FMat,FND,GDMat,GMat,GIMat,GSDMat,GSMat,HMat,IMat,Mat,SMat,SBMat,SDMat}\n",
    "import BIDMat.MatFunctions._\n",
    "import BIDMat.SciFunctions._\n",
    "import BIDMat.Solvers._\n",
    "import BIDMat.JPlotting._\n",
    "import BIDMach.Learner\n",
    "import BIDMach.models.{FM,GLM,KMeans,KMeansw,ICA,LDA,LDAgibbs,Model,NMF,RandomForest,SFA,SVD}\n",
    "import BIDMach.datasources.{DataSource,MatSource,FileSource,SFileSource}\n",
    "import BIDMach.mixins.{CosineSim,Perplexity,Top,L1Regularizer,L2Regularizer}\n",
    "import BIDMach.updaters.{ADAGrad,Batch,BatchNorm,IncMult,IncNorm,Telescoping}\n",
    "import BIDMach.causal.{IPTW}\n",
    "\n",
    "Mat.checkMKL\n",
    "Mat.checkCUDA\n",
    "Mat.setInline\n",
    "if (Mat.hasCUDA > 0) GPUmem"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And define the root directory for this dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "../data/MNIST8M/parts/"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val mdir = \"../data/MNIST8M/parts/\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Constrained Clustering. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "For this tutorial, we are going to evaluate the quality of clustering by using it for classification. We use a labeled dataset, and compute clusters of training samples using k-Means. Then we match new test samples to the clusters and find the best match. The label assigned to the new sample is the majority vote of the cluster. \n",
    "\n",
    "This method by itself doesnt work well. Clusters will often straddle label boundaries leading to poor labelings. Its better to force each cluster to have a single label. We do that by adding the labels in as very strong features before clustering. The label features cause samples with different labels to be very far apart. Far enough that k-Means will never assign them to the same cluster. The data we want looks like this:\n",
    "\n",
    "<pre>\n",
    "           Instance 0      Instance 1      Instance 2    ...\n",
    "           has label \"2\"   has label \"7\"   has label \"0\" ...\n",
    "           /    0               0           10000         ...\n",
    "          |     0               0               0         ...\n",
    "          | 10000               0               0         ...\n",
    "          |     0               0               0         ...\n",
    "label    /      0               0               0         ...\n",
    "features \\      0               0               0         ...\n",
    "(10)      |     0               0               0         ...\n",
    "          |     0           10000               0         ...\n",
    "          |     0               0               0         ...\n",
    "           \\    0               0               0         ...\n",
    "\n",
    "           /  128              19               5         ...\n",
    "          |    47              28               9         ...\n",
    "image    /     42             111              18         ...\n",
    "features \\     37             128              17         ...\n",
    "(784)     |    18             176              14         ...\n",
    "          |    ..              ..              ..\n",
    "\n",
    "</pre>\n",
    "\n",
    "\n",
    "We chose the label feature weights (here 10000) to force the distance between differently-labeled samples (2 * 10000^2) to be larger than the distance between two image samples (1000 * 256^2). This guarantees that points will not be assigned to a cluster containing a different label (assuming there is initially at least one cluster center with each label).  \n",
    "\n",
    "Even though these label features are present in cluster centroids after training, they dont affect matching at test time. Test images dont have the label features, and will match the closest cluster based only on image features. That cluster will have a unique label, which we then assign to the test point. \n",
    "\n",
    "The files containind data in this form are named \"alls00.fmat.lz4\", \"alls01.fmat.lz4\" etc. Since they contain both data and labels, we dont need to load label files separately. We can create a learner using a pattern for accessing these files:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "BIDMach.models.KMeans$FileOptions@178dc424"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val (mm, opts) = KMeans.learner(mdir+\"alls%02d.fmat.lz4\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The string \"%02d\" is a C/Scala format string that expands into a two-digit ASCII number to help with the enumeration.\n",
    "\n",
    "There are several new options that can tailor a files datasource, but we'll mostly use the defaults. One thing we will do is define the last file to use for training (number 70). This leaves us with some held-out files to use for testing. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opts.dim = 300\n",
    "opts.nend = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that the training data include image data and labels (0-9). K-Means is an unsupervised algorithm and if we used image data only KMeans will often build clusters containing different digit images. To produce cleaner clusters, and to facilitate classification later on, the <code>alls</code> data includes both labels in the first 10 rows, and image data in the remaining rows. The label features are scaled by a large constant factor. That means that images of different digits will be far apart in feature space. It effectively prevents different digits occuring in the same cluster. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Tuning Options"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following options are the important ones for tuning. For KMeans, batchSize has no effect on accracy since the algorithm uses all the data instances to perform an update. So you're free to tune it for best speed. Generally larger is better, as long as you dont use too much GPU ram. \n",
    "\n",
    "npasses is the number of passes over the dataset. Larger is typically better, but the model may overfit at some point. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "opts.batchSize = 20000\n",
    "opts.npasses = 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You invoke the learner the same way as before. You can change the options above after each run to optimize performance. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "pass= 0\n",
      "First pass random centroid initialization\n",
      " 4.00%, ll=0.00000, gf=9.646, secs=1.0, GB=0.13, MB/s=127.94, GPUmem=0.947386\n",
      "25.00%, ll=0.00000, gf=8.156, secs=2.3, GB=0.83, MB/s=351.54, GPUmem=0.947223\n",
      "48.00%, ll=0.00000, gf=8.199, secs=3.5, GB=1.52, MB/s=434.94, GPUmem=0.947223\n",
      "70.00%, ll=0.00000, gf=7.718, secs=5.0, GB=2.22, MB/s=447.77, GPUmem=0.947223\n",
      "91.00%, ll=0.00000, gf=7.749, secs=6.2, GB=2.92, MB/s=472.73, GPUmem=0.947223\n",
      "100.00%, ll=0.00000, gf=9.133, secs=6.3, GB=3.18, MB/s=504.69, GPUmem=0.947141\n",
      "pass= 1\n",
      " 4.00%, ll=-2996459.75000, gf=11.894, secs=7.2, GB=3.30, MB/s=455.84, GPUmem=0.945188\n",
      "25.00%, ll=-2997235.75000, gf=37.693, secs=7.6, GB=4.00, MB/s=525.44, GPUmem=0.945188\n",
      "48.00%, ll=-2978749.75000, gf=61.146, secs=8.0, GB=4.70, MB/s=589.03, GPUmem=0.945188\n",
      "70.00%, ll=-3000237.50000, gf=82.514, secs=8.3, GB=5.40, MB/s=646.77, GPUmem=0.945188\n",
      "91.00%, ll=-2996393.75000, gf=102.089, secs=8.7, GB=6.10, MB/s=699.70, GPUmem=0.945188\n",
      "100.00%, ll=-2984765.00000, gf=108.160, secs=8.8, GB=6.35, MB/s=718.15, GPUmem=0.945188\n",
      "pass= 2\n",
      " 4.00%, ll=-1882221.87500, gf=104.661, secs=9.4, GB=6.48, MB/s=688.16, GPUmem=0.945188\n",
      "25.00%, ll=-1900202.37500, gf=117.802, secs=10.1, GB=7.18, MB/s=712.79, GPUmem=0.945188\n",
      "48.00%, ll=-1888579.37500, gf=129.398, secs=10.7, GB=7.88, MB/s=734.75, GPUmem=0.945188\n",
      "70.00%, ll=-1909366.12500, gf=139.582, secs=11.4, GB=8.58, MB/s=753.73, GPUmem=0.945188\n",
      "91.00%, ll=-1904299.12500, gf=148.519, secs=12.0, GB=9.27, MB/s=769.94, GPUmem=0.945188\n",
      "100.00%, ll=-1910089.25000, gf=152.308, secs=12.2, GB=9.53, MB/s=781.94, GPUmem=0.945188\n",
      "pass= 3\n",
      " 4.00%, ll=-1846453.25000, gf=150.562, secs=12.5, GB=9.66, MB/s=771.35, GPUmem=0.945188\n",
      "25.00%, ll=-1852060.25000, gf=161.702, secs=12.9, GB=10.35, MB/s=802.80, GPUmem=0.945188\n",
      "48.00%, ll=-1835772.25000, gf=172.398, secs=13.3, GB=11.05, MB/s=833.39, GPUmem=0.945188\n",
      "70.00%, ll=-1844952.00000, gf=182.549, secs=13.6, GB=11.75, MB/s=862.47, GPUmem=0.945188\n",
      "91.00%, ll=-1854977.62500, gf=192.172, secs=14.0, GB=12.45, MB/s=890.04, GPUmem=0.945188\n",
      "100.00%, ll=-1837138.25000, gf=195.146, secs=14.1, GB=12.70, MB/s=899.84, GPUmem=0.945188\n",
      "pass= 4\n",
      " 4.00%, ll=-1821764.00000, gf=190.409, secs=14.6, GB=12.83, MB/s=877.64, GPUmem=0.945188\n",
      "25.00%, ll=-1825401.25000, gf=195.485, secs=15.3, GB=13.53, MB/s=886.15, GPUmem=0.945188\n",
      "48.00%, ll=-1817052.87500, gf=200.059, secs=15.9, GB=14.23, MB/s=893.58, GPUmem=0.945188\n",
      "70.00%, ll=-1841022.50000, gf=204.285, secs=16.6, GB=14.93, MB/s=900.48, GPUmem=0.945188\n",
      "91.00%, ll=-1830638.75000, gf=207.996, secs=17.2, GB=15.63, MB/s=906.01, GPUmem=0.945188\n",
      "100.00%, ll=-1837984.12500, gf=210.185, secs=17.4, GB=15.88, MB/s=913.38, GPUmem=0.945188\n",
      "pass= 5\n",
      " 4.00%, ll=-1816479.25000, gf=207.925, secs=17.7, GB=16.01, MB/s=903.69, GPUmem=0.945188\n",
      "25.00%, ll=-1822548.75000, gf=214.626, secs=18.1, GB=16.71, MB/s=923.17, GPUmem=0.945188\n",
      "48.00%, ll=-1805967.37500, gf=221.299, secs=18.5, GB=17.40, MB/s=942.92, GPUmem=0.945188\n",
      "70.00%, ll=-1814828.25000, gf=227.704, secs=18.8, GB=18.10, MB/s=961.86, GPUmem=0.945188\n",
      "91.00%, ll=-1826044.62500, gf=233.867, secs=19.2, GB=18.80, MB/s=980.08, GPUmem=0.945188\n",
      "100.00%, ll=-1807610.25000, gf=235.760, secs=19.3, GB=19.06, MB/s=986.64, GPUmem=0.945188\n",
      "pass= 6\n",
      " 4.00%, ll=-1810148.25000, gf=230.469, secs=19.9, GB=19.18, MB/s=964.84, GPUmem=0.945188\n",
      "25.00%, ll=-1809122.00000, gf=232.979, secs=20.5, GB=19.88, MB/s=968.42, GPUmem=0.945188\n",
      "48.00%, ll=-1801663.00000, gf=235.314, secs=21.2, GB=20.58, MB/s=971.69, GPUmem=0.945188\n",
      "70.00%, ll=-1825803.00000, gf=237.172, secs=21.9, GB=21.28, MB/s=973.39, GPUmem=0.945188\n",
      "91.00%, ll=-1814612.50000, gf=239.141, secs=22.5, GB=21.98, MB/s=975.89, GPUmem=0.945188\n",
      "100.00%, ll=-1822720.37500, gf=240.619, secs=22.7, GB=22.23, MB/s=981.07, GPUmem=0.945188\n",
      "pass= 7\n",
      " 4.00%, ll=-1805647.00000, gf=238.238, secs=23.0, GB=22.36, MB/s=971.79, GPUmem=0.945188\n",
      "25.00%, ll=-1811252.62500, gf=243.071, secs=23.4, GB=23.06, MB/s=986.34, GPUmem=0.945188\n",
      "48.00%, ll=-1794850.62500, gf=247.805, secs=23.7, GB=23.76, MB/s=1000.65, GPUmem=0.945188\n",
      "70.00%, ll=-1803667.00000, gf=252.407, secs=24.1, GB=24.46, MB/s=1014.57, GPUmem=0.945188\n",
      "91.00%, ll=-1815829.50000, gf=256.873, secs=24.5, GB=25.15, MB/s=1028.08, GPUmem=0.945188\n",
      "100.00%, ll=-1795750.25000, gf=258.238, secs=24.6, GB=25.41, MB/s=1032.97, GPUmem=0.945188\n",
      "pass= 8\n",
      " 4.00%, ll=-1805251.75000, gf=254.136, secs=25.1, GB=25.54, MB/s=1017.05, GPUmem=0.945188\n",
      "25.00%, ll=-1801781.87500, gf=255.422, secs=25.8, GB=26.23, MB/s=1018.11, GPUmem=0.945188\n",
      "48.00%, ll=-1794533.12500, gf=256.771, secs=26.4, GB=26.93, MB/s=1019.63, GPUmem=0.945188\n",
      "70.00%, ll=-1818941.25000, gf=258.007, secs=27.1, GB=27.63, MB/s=1020.88, GPUmem=0.945188\n",
      "91.00%, ll=-1807227.00000, gf=256.777, secs=28.0, GB=28.33, MB/s=1012.58, GPUmem=0.945188\n",
      "100.00%, ll=-1815120.50000, gf=257.889, secs=28.1, GB=28.58, MB/s=1016.61, GPUmem=0.945188\n",
      "pass= 9\n",
      " 4.00%, ll=-1799567.12500, gf=258.221, secs=28.2, GB=28.71, MB/s=1018.41, GPUmem=0.945188\n",
      "25.00%, ll=-1805670.12500, gf=258.981, secs=28.9, GB=29.41, MB/s=1018.17, GPUmem=0.945188\n",
      "48.00%, ll=-1789117.75000, gf=262.635, secs=29.2, GB=30.11, MB/s=1029.42, GPUmem=0.945188\n",
      "70.00%, ll=-1797674.00000, gf=266.208, secs=29.6, GB=30.81, MB/s=1040.43, GPUmem=0.945188\n",
      "91.00%, ll=-1810776.37500, gf=269.686, secs=30.0, GB=31.51, MB/s=1051.14, GPUmem=0.945188\n",
      "100.00%, ll=-1789468.62500, gf=270.755, secs=30.1, GB=31.76, MB/s=1055.08, GPUmem=0.945188\n",
      "Time=30.1030 secs, gflops=270.75\n"
     ]
    }
   ],
   "source": [
    "mm.train"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets extract the model as a Floating-point matrix. We included the category features for clustering to make sure that each cluster is a subset of images for one digit. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "      0      0      0      0  10000      0      0      0      0      0...\n",
       "      0      0      0      0      0  10000      0      0      0      0...\n",
       "      0      0      0  10000      0      0      0      0      0      0...\n",
       "      0      0      0      0  10000      0      0      0      0      0...\n",
       "      0      0      0      0      0      0      0      0  10000      0...\n",
       "      0      0  10000      0      0      0      0      0      0      0...\n",
       "      0      0      0      0      0      0      0      0      0  10000...\n",
       "      0      0      0      0      0      0      0      0  10000      0...\n",
       "     ..     ..     ..     ..     ..     ..     ..     ..     ..     ..\n"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val modelmat = FMat(mm.modelmat)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we build a 30 x 10 array of images to view the first 300 cluster centers as images."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "BufferedImage@2ce1269b: type = 10 ColorModel: #pixelBits = 8 numComponents = 1 color space = java.awt.color.ICC_ColorSpace@71ada121 transparency = 1 has alpha = false isAlphaPre = false ByteInterleavedRaster: width = 1680 height = 560 #numDataElements 1 dataOff[0] = 0"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val nx = 30\n",
    "val ny = 10\n",
    "val im = zeros(28,28)\n",
    "val allim = zeros(28*nx,28*ny)\n",
    "for (i<-0 until nx) {\n",
    "    for (j<-0 until ny) {\n",
    "        val slice = modelmat(i+nx*j,10->794)\n",
    "        im(?) = slice(?)\n",
    "        allim((28*i)->(28*(i+1)), (28*j)->(28*(j+1))) = im\n",
    "    }\n",
    "}\n",
    "show(allim kron ones(2,2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll predict using the closest cluster (or 1-NN if you like). Since we did constrained clustering, our data include the labels for each instance, but unlabeled test data doesnt have this. So we project the model matrix down to remove its first 10 features. Before doing this though we find the strongest label for each cluster so later on we can map from cluster id to label. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "      0      0      0      0  10000      0      0      0      0      0...\n",
       "      0      0      0      0      0  10000      0      0      0      0...\n",
       "      0      0      0  10000      0      0      0      0      0      0...\n",
       "      0      0      0      0  10000      0      0      0      0      0...\n",
       "      0      0      0      0      0      0      0      0  10000      0...\n",
       "      0      0  10000      0      0      0      0      0      0      0...\n",
       "      0      0      0      0      0      0      0      0      0  10000...\n",
       "      0      0      0      0      0      0      0      0  10000      0...\n",
       "     ..     ..     ..     ..     ..     ..     ..     ..     ..     ..\n"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val igood = find(sum(modelmat,2) > 100)                // find non-empty clusters\n",
    "val mmat = modelmat(igood,?)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "4,5,3,4,8,2,9,8,2,1,5,4,4,6,8,3,4,8,6,6,1,2,8,1,9,7,7,7,2,3,7,5,0,9,7,6,4,7,9,6,2,8,9,1,9,6,2,8,3,0,5,1,8,4,2,0,3,8,0,8,4,6,5,3,4,8,6,4,6,5,2,8,1,0,5,8,7,7,5,4,3,0,0,8,1,8,1,9,0,5,6,3,7,6,8,1,0,6,6,1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "val (dmy, catmap) = maxi2(mmat(?,0->10).t)                // Lookup the label for each cluster\n",
    "mm.model.modelmats(0) = mmat(?,10->mmat.ncols)            // Remove the label features\n",
    "mm.model.modelmats(1) = mm.modelmats(1)(igood,0)\n",
    "catmap(0->100)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next we define a predictor from the just-computed model and the testdata, with the preds files to catch the predictions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "10000"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val (pp, popts) = KMeans.predictor(mm.model, mdir+\"data%02d.fmat.lz4\", mdir+\"preds%02d.imat.lz4\")\n",
    "\n",
    "popts.nstart = 70                                      // start with file 70 as test data\n",
    "popts.nend = 80                                        // finish at file 79\n",
    "popts.ofcols = 100000                                  // Match number of samples per file to test file\n",
    "popts.batchSize = 10000"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Lets run the predictor"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Predicting\n",
      " 2.00%, ll=-101766204.00000, gf=17.354, secs=0.5, GB=0.06, MB/s=115.08, GPUmem=0.96\n",
      " 3.00%, ll=-101763576.00000, gf=25.243, secs=0.6, GB=0.09, MB/s=167.40, GPUmem=0.96\n",
      " 5.00%, ll=-101788140.00000, gf=39.873, secs=0.6, GB=0.16, MB/s=264.42, GPUmem=0.96\n",
      " 6.00%, ll=-101783104.00000, gf=46.590, secs=0.6, GB=0.19, MB/s=308.97, GPUmem=0.96\n",
      " 8.00%, ll=-101771032.00000, gf=59.019, secs=0.6, GB=0.25, MB/s=391.39, GPUmem=0.96\n",
      "10.00%, ll=-101780588.00000, gf=68.835, secs=0.7, GB=0.31, MB/s=456.48, GPUmem=0.96\n",
      "11.00%, ll=-101784624.00000, gf=73.890, secs=0.7, GB=0.34, MB/s=490.00, GPUmem=0.96\n",
      "12.00%, ll=-101783552.00000, gf=78.706, secs=0.7, GB=0.38, MB/s=521.94, GPUmem=0.96\n",
      "14.00%, ll=-101770100.00000, gf=87.805, secs=0.8, GB=0.44, MB/s=582.28, GPUmem=0.96\n",
      "16.00%, ll=-101776656.00000, gf=96.386, secs=0.8, GB=0.50, MB/s=639.18, GPUmem=0.96\n",
      "17.00%, ll=-101787176.00000, gf=104.060, secs=0.8, GB=0.56, MB/s=690.07, GPUmem=0.96\n",
      "19.00%, ll=-101787552.00000, gf=107.605, secs=0.8, GB=0.60, MB/s=713.58, GPUmem=0.96\n",
      "20.00%, ll=-101755660.00000, gf=62.145, secs=1.6, GB=0.66, MB/s=412.12, GPUmem=0.96\n",
      "22.00%, ll=-101796032.00000, gf=64.459, secs=1.6, GB=0.69, MB/s=427.46, GPUmem=0.96\n",
      "24.00%, ll=-101783900.00000, gf=68.952, secs=1.6, GB=0.75, MB/s=457.25, GPUmem=0.96\n",
      "25.00%, ll=-101770248.00000, gf=73.273, secs=1.7, GB=0.82, MB/s=485.91, GPUmem=0.96\n",
      "27.00%, ll=-101763712.00000, gf=75.417, secs=1.7, GB=0.85, MB/s=500.13, GPUmem=0.96\n",
      "28.00%, ll=-101793344.00000, gf=77.478, secs=1.7, GB=0.88, MB/s=513.80, GPUmem=0.96\n",
      "29.00%, ll=-101786272.00000, gf=79.501, secs=1.7, GB=0.91, MB/s=527.21, GPUmem=0.96\n",
      "30.00%, ll=-101786696.00000, gf=81.346, secs=1.7, GB=0.94, MB/s=539.45, GPUmem=0.96\n",
      "31.00%, ll=-101786160.00000, gf=74.151, secs=2.0, GB=0.97, MB/s=491.73, GPUmem=0.96\n",
      "32.00%, ll=-101754192.00000, gf=77.678, secs=2.0, GB=1.03, MB/s=515.12, GPUmem=0.96\n",
      "34.00%, ll=-101789520.00000, gf=79.360, secs=2.0, GB=1.07, MB/s=526.28, GPUmem=0.96\n",
      "35.00%, ll=-101788628.00000, gf=82.762, secs=2.1, GB=1.13, MB/s=548.84, GPUmem=0.96\n",
      "37.00%, ll=-101787560.00000, gf=84.364, secs=2.1, GB=1.16, MB/s=559.46, GPUmem=0.96\n",
      "39.00%, ll=-101755184.00000, gf=87.573, secs=2.1, GB=1.22, MB/s=580.74, GPUmem=0.96\n",
      "40.00%, ll=-101796928.00000, gf=89.015, secs=2.1, GB=1.25, MB/s=590.31, GPUmem=0.96\n",
      "41.00%, ll=-101792856.00000, gf=70.223, secs=2.8, GB=1.29, MB/s=465.69, GPUmem=0.96\n",
      "43.00%, ll=-101785048.00000, gf=72.805, secs=2.8, GB=1.35, MB/s=482.81, GPUmem=0.96\n",
      "44.00%, ll=-101747016.00000, gf=74.074, secs=2.8, GB=1.38, MB/s=491.22, GPUmem=0.96\n",
      "45.00%, ll=-101775712.00000, gf=76.595, secs=2.8, GB=1.44, MB/s=507.94, GPUmem=0.96\n",
      "46.00%, ll=-101791040.00000, gf=77.795, secs=2.9, GB=1.47, MB/s=515.90, GPUmem=0.96\n",
      "48.00%, ll=-101779064.00000, gf=79.008, secs=2.9, GB=1.51, MB/s=523.94, GPUmem=0.96\n",
      "49.00%, ll=-101786672.00000, gf=80.207, secs=2.9, GB=1.54, MB/s=531.89, GPUmem=0.96\n",
      "50.00%, ll=-101741160.00000, gf=81.309, secs=2.9, GB=1.57, MB/s=539.20, GPUmem=0.96\n",
      "51.00%, ll=-101774564.00000, gf=76.630, secs=3.2, GB=1.63, MB/s=508.17, GPUmem=0.96\n",
      "53.00%, ll=-101787384.00000, gf=77.716, secs=3.2, GB=1.66, MB/s=515.37, GPUmem=0.96\n",
      "54.00%, ll=-101776816.00000, gf=78.791, secs=3.2, GB=1.69, MB/s=522.51, GPUmem=0.96\n",
      "55.00%, ll=-101788208.00000, gf=79.856, secs=3.3, GB=1.72, MB/s=529.57, GPUmem=0.96\n",
      "57.00%, ll=-101753216.00000, gf=81.955, secs=3.3, GB=1.79, MB/s=543.48, GPUmem=0.96\n",
      "58.00%, ll=-101787264.00000, gf=82.964, secs=3.3, GB=1.82, MB/s=550.18, GPUmem=0.96\n",
      "59.00%, ll=-101792848.00000, gf=83.988, secs=3.3, GB=1.85, MB/s=556.97, GPUmem=0.96\n",
      "60.00%, ll=-101781872.00000, gf=84.951, secs=3.3, GB=1.88, MB/s=563.35, GPUmem=0.96\n",
      "61.00%, ll=-101779336.00000, gf=73.814, secs=3.9, GB=1.91, MB/s=489.50, GPUmem=0.96\n",
      "63.00%, ll=-101746584.00000, gf=75.634, secs=3.9, GB=1.98, MB/s=501.57, GPUmem=0.96\n",
      "64.00%, ll=-101785600.00000, gf=77.445, secs=4.0, GB=2.04, MB/s=513.58, GPUmem=0.96\n",
      "65.00%, ll=-101776904.00000, gf=78.341, secs=4.0, GB=2.07, MB/s=519.52, GPUmem=0.96\n",
      "66.00%, ll=-101786192.00000, gf=79.210, secs=4.0, GB=2.10, MB/s=525.28, GPUmem=0.96\n",
      "68.00%, ll=-101738880.00000, gf=80.092, secs=4.0, GB=2.13, MB/s=531.13, GPUmem=0.96\n",
      "70.00%, ll=-101774176.00000, gf=81.735, secs=4.1, GB=2.20, MB/s=542.02, GPUmem=0.96\n",
      "71.00%, ll=-101783492.00000, gf=74.504, secs=4.6, GB=2.26, MB/s=494.07, GPUmem=0.96\n",
      "73.00%, ll=-101785928.00000, gf=75.275, secs=4.6, GB=2.29, MB/s=499.19, GPUmem=0.96\n",
      "74.00%, ll=-101740944.00000, gf=76.025, secs=4.6, GB=2.32, MB/s=504.16, GPUmem=0.96\n",
      "76.00%, ll=-101773072.00000, gf=77.557, secs=4.6, GB=2.38, MB/s=514.32, GPUmem=0.96\n",
      "78.00%, ll=-101781872.00000, gf=79.052, secs=4.7, GB=2.45, MB/s=524.23, GPUmem=0.96\n",
      "79.00%, ll=-101783720.00000, gf=79.792, secs=4.7, GB=2.48, MB/s=529.14, GPUmem=0.96\n",
      "80.00%, ll=-101747504.00000, gf=80.493, secs=4.7, GB=2.51, MB/s=533.79, GPUmem=0.96\n",
      "81.00%, ll=-101763696.00000, gf=73.847, secs=5.2, GB=2.54, MB/s=489.72, GPUmem=0.96\n",
      "83.00%, ll=-101788504.00000, gf=75.206, secs=5.2, GB=2.60, MB/s=498.73, GPUmem=0.96\n",
      "85.00%, ll=-101786668.00000, gf=76.549, secs=5.3, GB=2.67, MB/s=507.64, GPUmem=0.96\n",
      "86.00%, ll=-101742464.00000, gf=77.229, secs=5.3, GB=2.70, MB/s=512.15, GPUmem=0.96\n",
      "87.00%, ll=-101762176.00000, gf=77.890, secs=5.3, GB=2.73, MB/s=516.53, GPUmem=0.96\n",
      "89.00%, ll=-101791016.00000, gf=79.186, secs=5.3, GB=2.79, MB/s=525.13, GPUmem=0.96\n",
      "91.00%, ll=-101786892.00000, gf=74.491, secs=5.8, GB=2.85, MB/s=493.99, GPUmem=0.96\n",
      "93.00%, ll=-101756068.00000, gf=75.709, secs=5.8, GB=2.92, MB/s=502.06, GPUmem=0.96\n",
      "95.00%, ll=-101789268.00000, gf=76.913, secs=5.8, GB=2.98, MB/s=510.05, GPUmem=0.96\n",
      "96.00%, ll=-101785280.00000, gf=77.510, secs=5.9, GB=3.01, MB/s=514.01, GPUmem=0.96\n",
      "98.00%, ll=-101767672.00000, gf=78.708, secs=5.9, GB=3.07, MB/s=521.96, GPUmem=0.96\n",
      "100.00%, ll=-101780368.00000, gf=79.854, secs=5.9, GB=3.14, MB/s=529.55, GPUmem=0.96\n",
      "Time=5.9230 secs, gflops=79.84\n"
     ]
    }
   ],
   "source": [
    "pp.predict "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The <code>preds</code> files now contains the numbers of the best-matching cluster centers. We still need to look up the category label for each one, and compare with the reference data. We'll do this one file at a time, so that our evaluation can scale to arbitrary problem sizes. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "   94068      49     582     269     108     613    1835     132     658...\n",
       "       8  111174     301     118     140      52     105     143     139...\n",
       "    1008    1405   91401     975     368     189     376    1634    1594...\n",
       "     241     701    1220   92966      75    2369     124     826    3043...\n",
       "     189    1671     401      66   86008      62     889     395     272...\n",
       "     713     456     293    5336     196   78245    1522     221    2419...\n",
       "    1147     626     483      70     144     910   94528       2     669...\n",
       "     173    2353    1076     199    1400     150       2   93470     269...\n",
       "      ..      ..      ..      ..      ..      ..      ..      ..      ..\n"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val totals = (popts.nstart until popts.nend).map(i => {\n",
    "                    val preds = loadIMat(mdir + \"preds%02d.imat.lz4\" format i);    // predicted centroids\n",
    "                    val cats = loadIMat(mdir + \"cat%02d.imat.lz4\" format i);       // reference labels\n",
    "                    val cpreds = catmap(preds);                                    // map centroid to label\n",
    "                    accum(cats.t \\ cpreds.t, 1.0, 10, 10)                          // form a confusion matrix\n",
    "}).reduce(_+_)\n",
    "\n",
    "totals"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the actual and predicted categories, we can compute a confusion matrix:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "     0.95588  0.00040690   0.0060295   0.0025629   0.0011322   0.0072072...\n",
       "  8.1293e-05     0.92320   0.0031183   0.0011242   0.0014677  0.00061138...\n",
       "    0.010243    0.011667     0.94691   0.0092893   0.0038579   0.0022221...\n",
       "   0.0024489   0.0058212    0.012639     0.88573  0.00078625    0.027853...\n",
       "   0.0019205    0.013876   0.0041543  0.00062881     0.90166  0.00072895...\n",
       "   0.0072452   0.0037867   0.0030355    0.050838   0.0020547     0.91995...\n",
       "    0.011655   0.0051984   0.0050038  0.00066692   0.0015096    0.010699...\n",
       "   0.0017580    0.019540    0.011147   0.0018960    0.014677   0.0017636...\n",
       "          ..          ..          ..          ..          ..          ..\n"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val conf = float(totals / sum(totals))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now lets create an image by multiplying each confusion matrix cell by a white square:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "BufferedImage@6aec4871: type = 10 ColorModel: #pixelBits = 8 numComponents = 1 color space = java.awt.color.ICC_ColorSpace@71ada121 transparency = 1 has alpha = false isAlphaPre = false ByteInterleavedRaster: width = 320 height = 320 #numDataElements 1 dataOff[0] = 0"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "show((conf * 250f) ⊗ ones(32,32))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Its useful to isolate the correct classification rate by digit, which is:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "0.95588,0.92320,0.94691,0.88573,0.90166,0.91995,0.94395,0.92112,0.89660,0.83081"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "val dacc = getdiag(conf).t"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can take the mean of the diagonal accuracies to get an overall accuracy for this model. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": []
    },
    {
     "data": {
      "text/plain": [
       "0.91258"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mean(dacc)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the experiment again with a larger number of clusters (3000, then 30000). You should reduce the batchSize option to 20000 to avoid memory problems.\n",
    "\n",
    "Include the training time output by the call to <code>nn.train</code> but not the evaluation time. Rerun and fill out the table below: \n",
    "\n",
    "<table>\n",
    "<tr>\n",
    "<th>KMeans Clusters</th>\n",
    "<th>Training time</th>\n",
    "<th>Avg. gflops</th>\n",
    "<th>Accuracy</th>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>300</td>\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>3000</td>\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "</tr>\n",
    "<tr>\n",
    "<td>30000</td>\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "<td>...</td>\n",
    "</tr>\n",
    "</table>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "name": "scala",
   "version": "2.11.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}
